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Volume & Issue no: Volume 6, Issue 2, March - April 2017

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Title:
Analyzing Youtube Data Using K Means Clustering
Author Name:
G. Ravali, P.Swathi Moulika, A.A parna, J.Abhinaya, T.Anuradha
Abstract:
Abstract YouTube is one of the most popular social networking sites which have millions of users posting and viewing different kinds of videos. Having different channels posting different categories of videos regularly and users from different parts of the globe giving feedback on these videos, YouTube is one of the best source of big data. Previous analysis on YouTube data are dealing with users’ sentimental analysis related to different issues. This paper mainly concentrates on analysing YouTube data of a taken time period in terms of different parameters like category, channel and location. It deals with analysing YouTube data based on data mining clustering algorithm named K-Means. As the existing relational databases are lagging behind handling voluminous data, people started moving towards NOSQL databases. One such popular NOSQL database is MongoDB which has high scalability. Results are analysed visually by using tools like Weka, Rapidminer and Tableau. Keywords — YouTube, big data mining, k means clustering, MongoDB , NOSQL,JSON
Cite this article:
G. Ravali, P.Swathi Moulika, A.A parna, J.Abhinaya, T.Anuradha , " Analyzing Youtube Data Using K Means Clustering" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 6, Issue 2, March - April 2017 , pp. 062-066 , ISSN 2278-6856.
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NOTE: Authors note that paper cannot be withdrawn at any condition once it is accepted. The Team of IJETTCS advise you, do not submit same article to the multiple journals simultaneously. This may create a problem for you. Please wait for review report which will take maximum 01 to 02 week. 

 

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
ISSN 2278-6856
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